How to fit a curve to first part of some data points by using Normal Distribution function?
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Hello
I was wondering how to fit a curve to some data points by using normal distribution function. Assume that we already know that:
1- We just have some first part of the data points.
2- the pattern of rest of these data points will change in manner of normal distribution function.
So for example:
X = [1 2 3 4 5 6 7 8 9];
Y = [0 0.5 0.9 3 3.1 5.2 5.8 5.9 6.5];
Curve_Estimation = smooth (X, Y); % Just an estimation to show the posible distributin form
plot (X, Y, X, Curve_Estimation, 'linewidth' , 2);
In other words, the question is how to predict the changes in data points?
Thank you
3 件のコメント
J. Alex Lee
2020 年 3 月 18 日
If I understand correctly, you indeed have a fitting (regression) problem. You want to fit the function
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/277864/image.png)
to your (x,y) data. However, your y data are not a properly normalized cdf (you have values > 1), so you will need an additional constant in front.
Based on your sample data, I don't think this strategy will give you very good results; since your data don't reach into the plateau, and it looks noisy, there will probably be many combinations of the scaling constant and other parameters that will give you roughly the same quality fits. In general, I'm not sure it makes sense to pose a distribution fitting problem in terms of only partially supplied distribution...it's asking to infer statistics from an insanely biased sample set (find the actual mean of this normally distributed data, but i'm only going to give you data where the values are all less than the mean - impossible?).
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